Small Object Segmentation

Small object segmentation focuses on accurately identifying and delineating small objects within images, a challenging task due to limited pixel information and the inherent limitations of many deep learning architectures. Current research emphasizes novel network architectures, such as variations of U-Net and Transformers, incorporating attention mechanisms and multi-scale feature aggregation to improve the detection and segmentation of these small objects. This area is crucial for applications across diverse fields, including medical image analysis (e.g., detecting small tumors or lesions), remote sensing (e.g., classifying small land cover features), and astronomy (e.g., identifying cosmic rays), where accurate segmentation of small details is critical for diagnosis, analysis, and decision-making.